Humans observe and interact with the world to acquire knowledge. However,
most existing machine reading comprehension (MRC) tasks miss the interactive,
information-seeking component of comprehension. Such tasks present models with
static documents that contain all necessary information, usually concentrated
in a single short substring. Thus, models can achieve strong performance
through simple word- and phrase-based pattern matching. We address this problem
by formulating a novel text-based question answering task: Question Answering
with Interactive Text (QAit). In QAit, an agent must interact with a partially
observable text-based environment to gather information required to answer
questions. QAit poses questions about the existence, location, and attributes
of objects found in the environment. The data is built using a text-based game
generator that defines the underlying dynamics of interaction with the
environment. We propose and evaluate a set of baseline models for the QAit task
that includes deep reinforcement learning agents. Experiments show that the
task presents a major challenge for machine reading systems, while humans solve
it with relative ease.

该论文提出了一种基于交互的文本问答任务 QAit，通过使用基于深度强化学习的智能体与部分可观察的文本环境交互来获取答案所需的信息，提高了机器阅读系统在问答任务中的表现。